rf406_cattocatfuncs.C: 'DATA AND CATEGORIES' RooFit tutorial macro #406

//////////////////////////////////////////////////////////////////////////
//
// 'DATA AND CATEGORIES' RooFit tutorial macro #406
// 
// Demonstration of discrete-->discrete (invertable) functions
//
//
//
// 07/2008 - Wouter Verkerke 
// 
/////////////////////////////////////////////////////////////////////////

#ifndef __CINT__
#include "RooGlobalFunc.h"
#endif
#include "RooRealVar.h"
#include "RooDataSet.h"
#include "RooPolynomial.h"
#include "RooCategory.h"
#include "RooMappedCategory.h"
#include "RooMultiCategory.h"
#include "RooSuperCategory.h"
#include "Roo1DTable.h"
#include "TCanvas.h"
#include "TAxis.h"
#include "RooPlot.h"
using namespace RooFit ;


void rf406_cattocatfuncs()
{
  // C o n s t r u c t  t w o   c a t e g o r i e s
  // ----------------------------------------------

  // Define a category with labels only
  RooCategory tagCat("tagCat","Tagging category") ;
  tagCat.defineType("Lepton") ;
  tagCat.defineType("Kaon") ;
  tagCat.defineType("NetTagger-1") ;
  tagCat.defineType("NetTagger-2") ;
  tagCat.Print() ;

  // Define a category with explicitly numbered states
  RooCategory b0flav("b0flav","B0 flavour eigenstate") ;
  b0flav.defineType("B0",-1) ;
  b0flav.defineType("B0bar",1) ;
  b0flav.Print() ;

  // Construct a dummy dataset with random values of tagCat and b0flav
  RooRealVar x("x","x",0,10) ;
  RooPolynomial p("p","p",x) ;
  RooDataSet* data = p.generate(RooArgSet(x,b0flav,tagCat),10000) ;



  // C r e a t e   a   c a t - > c a t   m  a p p i n g   c a t e g o r y 
  // ---------------------------------------------------------------------

  // A RooMappedCategory is category->category mapping function based on string expression
  // The constructor takes an input category an a default state name to which unassigned
  // states are mapped
  RooMappedCategory tcatType("tcatType","tagCat type",tagCat,"Cut based") ;

  // Enter fully specified state mappings
  tcatType.map("Lepton","Cut based") ;
  tcatType.map("Kaon","Cut based") ;

  // Enter a wilcard expression mapping
  tcatType.map("NetTagger*","Neural Network") ;

  // Make a table of the mapped category state multiplicit in data
  Roo1DTable* mtable = data->table(tcatType) ;
  mtable->Print("v") ;



  // C r e a t e   a   c a t   X   c a t   p r o d u c t   c a t e g o r y 
  // ----------------------------------------------------------------------

  // A SUPER-category is 'product' of _lvalue_ categories. The state names of a super
  // category is a composite of the state labels of the input categories
  RooSuperCategory b0Xtcat("b0Xtcat","b0flav X tagCat",RooArgSet(b0flav,tagCat)) ;

  // Make a table of the product category state multiplicity in data
  Roo1DTable* stable = data->table(b0Xtcat) ;
  stable->Print("v") ;

  // Since the super category is an lvalue, assignment is explicitly possible
  b0Xtcat.setLabel("{B0bar;Lepton}") ;



  // A MULTI-category is a 'product' of any category (function). The state names of a super
  // category is a composite of the state labels of the input categories
  RooMultiCategory b0Xttype("b0Xttype","b0flav X tagType",RooArgSet(b0flav,tcatType)) ;
  
  // Make a table of the product category state multiplicity in data
  Roo1DTable* xtable = data->table(b0Xttype) ;
  xtable->Print("v") ;


}